Economic Capital Allocation under Coherent Market Liquidity Constraints

The average annual profits before fees of the $10 billion plus Renaissance Technologies’ hybrid Medallion “Leveraged, High Frequency, Artificial Intelligence (LHFAI)” trading hedge fund between 1988 and 2019 were about 66 percent. Total trading profits during this period were over $100 billion. The fund has never had a losing year. The fund is not open to the general public. First, distinctions among, in more or less historical order, the traditional market-maker trading model, the hedge fund trading model, the artificial intelligence trading model, and the hybrid LHFAI trading model are discussed. Second, the micro components of the LHFAI trading model are explained in the context of Renaissance Technologies’ Medallion Fund. Third, key positive contributions of the model with respect to profitability, low annual volatility, market liquidity, and intellectual property development; negative ethical issues concerning exclusive access, tax fairness, financial transparency, shared responsibility for losses and systemic risk, and short vs. long-term capital allocation are discussed. Potential reforms that retain the positives, reduce the negatives, and that could positively transform the model are discussed. Fourth, potential impacts that the potential reforms might have on the macro LHFAI form of finance capitalism and the larger finance capitalism political-economic system are considered. Fifth, conclusions are offered and discussed.

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THE EFFECTS OF BANK CONSOLIDATION ON RISK CAPITAL ALLOCATION AND MARKET LIQUIDITY

The Journal of Financial Research ◽

10.1111/j.1475-6803.2006.00178.x ◽

2006 ◽

Vol 29 (2) ◽

pp. 271-291 ◽

Cited By ~ 7

Author(s):

Chris D'Souza ◽

Alexandra Lai

Keyword(s):

Capital Allocation ◽

Market Liquidity ◽

Risk Capital ◽

Bank Consolidation ◽

Risk Capital Allocation

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ON SARMANOV MIXED ERLANG RISKS IN INSURANCE APPLICATIONS

Astin Bulletin ◽

10.1017/asb.2014.24 ◽

2014 ◽

Vol 45 (1) ◽

pp. 175-205 ◽

Cited By ~ 14

Author(s):

Enkelejd Hashorva ◽

Gildas Ratovomirija

Keyword(s):

North American ◽

Stochastic Models ◽

Capital Allocation ◽

Economic Capital ◽

Individual Risk ◽

Dependence Structure ◽

Simulation Studies ◽

Numerical Examples ◽

Dependence Measures ◽

Analytical Expressions

AbstractIn this paper we consider an extension to the aggregation of the FGM mixed Erlang risks, proposed by Cossette et al. (2013 Insurance: Mathematics and Economics, 52, 560–572), in which we introduce the Sarmanov distribution to model the dependence structure. For our framework, we demonstrate that the aggregated risk belongs to the class of Erlang mixtures. Following results from S. C. K. Lee and X. S. Lin (2010 North American Actuarial Journal, 14(1) 107–130), G. E. Willmot and X. S. Lin (2011 Applied Stochastic Models in Business and Industry, 27(1) 8–22), analytical expressions of the contribution of each individual risk to the economic capital for the entire portfolio are derived under both the TVaR and the covariance capital allocation principle. By analysing the commonly used dependence measures, we also show that the dependence structure is wide and flexible. Numerical examples and simulation studies illustrate the tractability of our approach.

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